224 research outputs found
Perceptual audio loss function for deep learning
PESQ and POLQA , are standards are standards for automated assessment of
voice quality of speech as experienced by human beings. The predictions of
those objective measures should come as close as possible to subjective quality
scores as obtained in subjective listening tests. Wavenet is a deep neural
network originally developed as a deep generative model of raw audio
wave-forms. Wavenet architecture is based on dilated causal convolutions, which
exhibit very large receptive fields. In this short paper we suggest using the
Wavenet architecture, in particular its large receptive filed in order to learn
PESQ algorithm. By doing so we can use it as a differentiable loss function for
speech enhancement
Spatially-Adaptive Reconstruction in Computed Tomography Based on Statistical Learning
We propose a direct reconstruction algorithm for Computed Tomography, based
on a local fusion of a few preliminary image estimates by means of a non-linear
fusion rule. One such rule is based on a signal denoising technique which is
spatially adaptive to the unknown local smoothness. Another, more powerful
fusion rule, is based on a neural network trained off-line with a high-quality
training set of images. Two types of linear reconstruction algorithms for the
preliminary images are employed for two different reconstruction tasks. For an
entire image reconstruction from full projection data, the proposed scheme uses
a sequence of Filtered Back-Projection algorithms with a gradually growing
cut-off frequency. To recover a Region Of Interest only from local projections,
statistically-trained linear reconstruction algorithms are employed. Numerical
experiments display the improvement in reconstruction quality when compared to
linear reconstruction algorithms.Comment: Submitted to IEEE Transactions on Image Processin
Accelerating Multigrid Optimization via SESOP
A merger of two optimization frameworks is introduced: SEquential Subspace
OPtimization (SESOP) with the MultiGrid (MG) optimization. At each iteration of
the algorithm, search directions implied by the coarse-grid correction (CGC)
process of MG are added to the low dimensional search-spaces of SESOP, which
include the (preconditioned) gradient and search directions involving the
previous iterates (so-called history). The resulting accelerated technique is
called SESOP-MG. The asymptotic convergence factor of the two-level version of
SESOP-MG (dubbed SESOP-TG) is studied via Fourier mode analysis for linear
problems, i.e., optimization of quadratic functionals. Numerical tests on
linear and nonlinear problems demonstrate the effectiveness of the approach.Comment: 7 figures, 2 table
Designing and using prior knowledge for phase retrieval
In this work we develop an algorithm for signal reconstruction from the
magnitude of its Fourier transform in a situation where some (non-zero) parts
of the sought signal are known. Although our method does not assume that the
known part comprises the boundary of the sought signal, this is often the case
in microscopy: a specimen is placed inside a known mask, which can be thought
of as a known light source that surrounds the unknown signal. Therefore, in the
past, several algorithms were suggested that solve the phase retrieval problem
assuming known boundary values. Unlike our method, these methods do rely on the
fact that the known part is on the boundary. Besides the reconstruction method
we give an explanation of the phenomena observed in previous work: the
reconstruction is much faster when there is more energy concentrated in the
known part. Quite surprisingly, this can be explained using our previous
results on phase retrieval with approximately known Fourier phase
Phase retrieval combined with digital holography
We present a new method for real- and complex-valued image reconstruction
from two intensity measurements made in the Fourier plane: the Fourier
magnitude of the unknown image, and the intensity of the interference pattern
arising from superimposition of the original signal with a reference beam. This
approach can provide significant advantages in digital holography since it
poses less stringent requirements on the reference beam. In particular, it does
not require spatial separation between the sought signal and the reference
beam. Moreover, the reference beam need not be known precisely, and in fact,
may contain severe errors, without leading to a deterioration in the
reconstruction quality. Numerical simulations are presented to demonstrate the
speed and quality of reconstruction
A Deep Learning Approach to Block-based Compressed Sensing of Images
Compressed sensing (CS) is a signal processing framework for efficiently
reconstructing a signal from a small number of measurements, obtained by linear
projections of the signal. Block-based CS is a lightweight CS approach that is
mostly suitable for processing very high-dimensional images and videos: it
operates on local patches, employs a low-complexity reconstruction operator and
requires significantly less memory to store the sensing matrix. In this paper
we present a deep learning approach for block-based CS, in which a
fully-connected network performs both the block-based linear sensing and
non-linear reconstruction stages. During the training phase, the sensing matrix
and the non-linear reconstruction operator are \emph{jointly} optimized, and
the proposed approach outperforms state-of-the-art both in terms of
reconstruction quality and computation time. For example, at a 25% sensing rate
the average PSNR advantage is 0.77dB and computation time is over 200-times
faster
Trainlets: Dictionary Learning in High Dimensions
Sparse representations has shown to be a very powerful model for real world
signals, and has enabled the development of applications with notable
performance. Combined with the ability to learn a dictionary from signal
examples, sparsity-inspired algorithms are often achieving state-of-the-art
results in a wide variety of tasks. Yet, these methods have traditionally been
restricted to small dimensions mainly due to the computational constraints that
the dictionary learning problem entails. In the context of image processing,
this implies handling small image patches. In this work we show how to
efficiently handle bigger dimensions and go beyond the small patches in
sparsity-based signal and image processing methods. We build our approach based
on a new cropped wavelet decomposition, which enables a multi-scale analysis
with virtually no border effects. We then employ this as the base dictionary
within a double sparsity model to enable the training of adaptive dictionaries.
To cope with the increase of training data, while at the same time improving
the training performance, we present an Online Sparse Dictionary Learning
(OSDL) algorithm to train this model effectively, enabling it to handle
millions of examples. This work shows that dictionary learning can be up-scaled
to tackle a new level of signal dimensions, obtaining large adaptable atoms
that we call trainlets
Towards CT-quality Ultrasound Imaging using Deep Learning
The cost-effectiveness and practical harmlessness of ultrasound imaging have
made it one of the most widespread tools for medical diagnosis. Unfortunately,
the beam-forming based image formation produces granular speckle noise,
blurring, shading and other artifacts. To overcome these effects, the ultimate
goal would be to reconstruct the tissue acoustic properties by solving a full
wave propagation inverse problem. In this work, we make a step towards this
goal, using Multi-Resolution Convolutional Neural Networks (CNN). As a result,
we are able to reconstruct CT-quality images from the reflected ultrasound
radio-frequency(RF) data obtained by simulation from real CT scans of a human
body. We also show that CNN is able to imitate existing computationally heavy
despeckling methods, thereby saving orders of magnitude in computations and
making them amenable to real-time applications
Self-supervised learning of inverse problem solvers in medical imaging
In the past few years, deep learning-based methods have demonstrated enormous
success for solving inverse problems in medical imaging. In this work, we
address the following question:\textit{Given a set of measurements obtained
from real imaging experiments, what is the best way to use a learnable model
and the physics of the modality to solve the inverse problem and reconstruct
the latent image?} Standard supervised learning based methods approach this
problem by collecting data sets of known latent images and their corresponding
measurements. However, these methods are often impractical due to the lack of
availability of appropriately sized training sets, and, more generally, due to
the inherent difficulty in measuring the "groundtruth" latent image. In light
of this, we propose a self-supervised approach to training inverse models in
medical imaging in the absence of aligned data. Our method only requiring
access to the measurements and the forward model at training. We showcase its
effectiveness on inverse problems arising in accelerated magnetic resonance
imaging (MRI).Comment: preprin
Learning beamforming in ultrasound imaging
Medical ultrasound (US) is a widespread imaging modality owing its popularity
to cost efficiency, portability, speed, and lack of harmful ionizing radiation.
In this paper, we demonstrate that replacing the traditional ultrasound
processing pipeline with a data-driven, learnable counterpart leads to
significant improvement in image quality. Moreover, we demonstrate that greater
improvement can be achieved through a learning-based design of the transmitted
beam patterns simultaneously with learning an image reconstruction pipeline. We
evaluate our method on an in-vivo first-harmonic cardiac ultrasound dataset
acquired from volunteers and demonstrate the significance of the learned
pipeline and transmit beam patterns on the image quality when compared to
standard transmit and receive beamformers used in high frame-rate US imaging.
We believe that the presented methodology provides a fundamentally different
perspective on the classical problem of ultrasound beam pattern design
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